Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86089370" target="_blank" >RIV/61989100:27740/13:86089370 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/NaBIC.2013.6617856" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2013.6617856</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/NaBIC.2013.6617856" target="_blank" >10.1109/NaBIC.2013.6617856</a>

Result language
angličtina
Original language name
Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique
Original language description
Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on apre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overal
Czech name
—
Czech description
—

Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013
ISBN
978-1-4799-1415-9
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
169-174
Publisher name
Elsevier
Place of publication
New York
Event location
Fargo
Event date
Aug 12, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)